Computing Mantel tests for natural examples of composition requires us to select a set of natural language expressions for which we can compute semantic distances between any pair.
Taking inspiration from hill-etal-2016-learning-understand, we use dictionary definitions.
Indeed, in dictionaries, the meaning of the word being defined (or “definiendum”; pl. “definienda”) is arguably also the meaning of the definition gloss.
Hence, we can equate the semantic distance between two glosses to the semantic distance between the corresponding definienda.
Distributional semantics models [1]} allow us to conveniently compute the latter: they represent words as vectors in a space over which we can use metrics such as cosine similarity or Euclidean distance, and have moreover been found to match human intuitions [2]}.
